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Record W2016534719 · doi:10.1080/09589236.2011.593326

‘Food is culture, but it's also power’: the role of food in ethnic and gender identity construction among Goan Canadian women

2011· article· en· W2016534719 on OpenAlexaffabout
Andrea D’Sylva, Brenda L. Beagan

Bibliographic record

VenueJournal of Gender Studies · 2011
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCulinary Culture and Tourism
Canadian institutionsDalhousie University
Fundersnot available
KeywordsOppressionGender studiesEthnic groupSociologyIdentity (music)Power (physics)Context (archaeology)DiasporaPolitical scienceAnthropologyHistoryPoliticsLaw

Abstract

fetched live from OpenAlex

Foodwork and women's primary responsibility for foodwork have long been interpreted by feminist scholars as a site of gender oppression for women; yet the gendered meanings of foodwork are complicated when race, diaspora and ethnic identity are also taken into account. This article examines the meaning of food and foodwork for Goan women in Toronto, Canada, and the role of food in creating and maintaining distinctly gendered ethnic identities. Catholic Goan identity, born from Portuguese colonization of an area in what is now Western India, has few unique markers of ethnic distinction from other Indians. In this context Goan cuisine takes on a particular symbolic significance. In this qualitative study with first-generation Canadian Goan women (N = 13) the gendered role of women in foodwork was seen as having particular power or ‘currency’ within the family and community, valued for fostering and supporting Goan identity. We argue that the same foodwork practices that constitute gendered oppression for women may simultaneously confer a form of ‘culinary capital’ within the social arena of their own diasporic community.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.039
Threshold uncertainty score0.285

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0260.019
Scholarly communication0.0050.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.078
GPT teacher head0.263
Teacher spread0.186 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations117
Published2011
Admission routes2
Has abstractyes

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